Examples/RegistrationITKv4/ImageRegistration9.cxx

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 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
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 *         https://www.apache.org/licenses/LICENSE-2.0.txt
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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySliceBorder20.png}
//    INPUTS:  {BrainProtonDensitySliceR10X13Y17.png}
//    OUTPUTS: {ImageRegistration9Output.png}
//    OUTPUTS: {ImageRegistration9DifferenceBefore.png}
//    OUTPUTS: {ImageRegistration9DifferenceAfter.png}
//    ARGUMENTS:    1.0 300
//  Software Guide : EndCommandLineArgs
 
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{AffineTransform}
// for performing registration in $2D$. The example code is, for the most
// part, identical to that in \ref{sec:InitializingRegistrationWithMoments}.
// The main difference is the use of the AffineTransform here instead of the
// \doxygen{Euler2DTransform}. We will focus on the most
// relevant changes in the current code and skip the basic elements already
// explained in previous examples.
//
// \index{itk::AffineTransform}
//
// Software Guide : EndLatex
 
#include "itkImageRegistrationMethodv4.h"
#include "itkMeanSquaresImageToImageMetricv4.h"
#include "itkRegularStepGradientDescentOptimizerv4.h"
 
 
#include "itkCenteredTransformInitializer.h"
 
//  Software Guide : BeginLatex
//
//  Let's start by including the header file of the AffineTransform.
//
//  \index{itk::AffineTransform!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkAffineTransform.h"
// Software Guide : EndCodeSnippet
 
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
 
 
//
//  The following piece of code implements an observer
//  that will monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
  using Self = CommandIterationUpdate;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);
 
protected:
  CommandIterationUpdate() = default;
 
public:
  using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;
  using OptimizerPointer = const OptimizerType *;
 
  void
  Execute(itk::Object * caller, const itk::EventObject & event) override
  {
    Execute((const itk::Object *)caller, event);
  }
 
  void
  Execute(const itk::Object * object, const itk::EventObject & event) override
  {
    auto optimizer = static_cast<OptimizerPointer>(object);
    if (!itk::IterationEvent().CheckEvent(&event))
    {
      return;
    }
    std::cout << optimizer->GetCurrentIteration() << "   ";
    std::cout << optimizer->GetValue() << "   ";
    std::cout << optimizer->GetCurrentPosition();
 
    // Print the angle for the trace plot
    vnl_matrix<double> p(2, 2);
    p[0][0] = static_cast<double>(optimizer->GetCurrentPosition()[0]);
    p[0][1] = static_cast<double>(optimizer->GetCurrentPosition()[1]);
    p[1][0] = static_cast<double>(optimizer->GetCurrentPosition()[2]);
    p[1][1] = static_cast<double>(optimizer->GetCurrentPosition()[3]);
    vnl_svd<double>    svd(p);
    vnl_matrix<double> r(2, 2);
    r = svd.U() * vnl_transpose(svd.V());
    const double angle = std::asin(r[1][0]);
    std::cout << " AffineAngle: " << angle * 180.0 / itk::Math::pi
              << std::endl;
  }
};
 
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << "   fixedImageFile  movingImageFile " << std::endl;
    std::cerr << "   outputImagefile  [differenceBeforeRegistration] "
              << std::endl;
    std::cerr << "   [differenceAfterRegistration] " << std::endl;
    std::cerr << "   [stepLength] [maxNumberOfIterations] " << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  We then define the types of the images to be registered.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 2;
  using PixelType = float;
 
  using FixedImageType = itk::Image<PixelType, Dimension>;
  using MovingImageType = itk::Image<PixelType, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The transform type is instantiated using the code below. The template
  //  parameters of this class are the representation type of the space
  //  coordinates and the space dimension.
  //
  //  \index{itk::AffineTransform!Instantiation}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using TransformType = itk::AffineTransform<double, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;
  using MetricType =
    itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType>;
  using RegistrationType = itk::
    ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
 
  auto metric = MetricType::New();
  auto optimizer = OptimizerType::New();
  auto registration = RegistrationType::New();
 
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
 
 
  //  Software Guide : BeginLatex
  //
  //  The transform object is constructed below and is initialized before the
  //  registration process starts.
  //
  //  \index{itk::AffineTransform!New()}
  //  \index{itk::AffineTransform!Pointer}
  //  \index{itk::RegistrationMethodv4!SetTransform()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto transform = TransformType::New();
  // Software Guide : EndCodeSnippet
 
 
  using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
  using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
  auto fixedImageReader = FixedImageReaderType::New();
  auto movingImageReader = MovingImageReaderType::New();
  fixedImageReader->SetFileName(argv[1]);
  movingImageReader->SetFileName(argv[2]);
 
 
  registration->SetFixedImage(fixedImageReader->GetOutput());
  registration->SetMovingImage(movingImageReader->GetOutput());
 
 
  //  Software Guide : BeginLatex
  //
  //  In this example, we again use the
  //  \doxygen{CenteredTransformInitializer} helper class in order to compute
  //  reasonable values for the initial center of rotation and the
  //  translations. The initializer is set to use the center of mass of each
  //  image as the initial correspondence correction.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using TransformInitializerType =
    itk::CenteredTransformInitializer<TransformType,
                                      FixedImageType,
                                      MovingImageType>;
  auto initializer = TransformInitializerType::New();
  initializer->SetTransform(transform);
  initializer->SetFixedImage(fixedImageReader->GetOutput());
  initializer->SetMovingImage(movingImageReader->GetOutput());
  initializer->MomentsOn();
  initializer->InitializeTransform();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Now we pass the transform object to the registration filter, and it will
  //  be grafted to the output transform of the registration filter by
  //  updating its parameters during the the registration process.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  registration->SetInitialTransform(transform);
  registration->InPlaceOn();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Keeping in mind that the scale of units in scaling, rotation and
  //  translation are quite different, we take advantage of the scaling
  //  functionality provided by the optimizers. We know that the first $N
  //  \times N$ elements of the parameters array correspond to the rotation
  //  matrix factor, and the last $N$ are the components of the translation to
  //  be applied after multiplication with the matrix is performed.
  //
  //  Software Guide : EndLatex
 
 
  double translationScale = 1.0 / 1000.0;
 
  if (argc > 8)
  {
    translationScale = std::stod(argv[8]);
  }
 
 
  // Software Guide : BeginCodeSnippet
  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
 
  optimizerScales[0] = 1.0;
  optimizerScales[1] = 1.0;
  optimizerScales[2] = 1.0;
  optimizerScales[3] = 1.0;
  optimizerScales[4] = translationScale;
  optimizerScales[5] = translationScale;
 
  optimizer->SetScales(optimizerScales);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  We also set the usual parameters of the optimization method. In this
  //  case we are using an
  //  \doxygen{RegularStepGradientDescentOptimizerv4} as before. Below, we
  //  define the optimization parameters like learning rate (initial step
  //  length), minimum step length and number of iterations. These last two
  //  act as stopping criteria for the optimization.
  //
  //  Software Guide : EndLatex
 
  double steplength = 1.0;
 
  if (argc > 6)
  {
    steplength = std::stod(argv[6]);
  }
 
 
  unsigned int maxNumberOfIterations = 300;
 
  if (argc > 7)
  {
    maxNumberOfIterations = std::stoi(argv[7]);
  }
 
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetLearningRate(steplength);
  optimizer->SetMinimumStepLength(0.0001);
  optimizer->SetNumberOfIterations(maxNumberOfIterations);
  // Software Guide : EndCodeSnippet
 
 
  // Create the Command observer and register it with the optimizer.
  //
  auto observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
 
  // One level registration process without shrinking and smoothing.
  //
  constexpr unsigned int numberOfLevels = 1;
 
  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize(1);
  shrinkFactorsPerLevel[0] = 1;
 
  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize(1);
  smoothingSigmasPerLevel[0] = 0;
 
  registration->SetNumberOfLevels(numberOfLevels);
  registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
  registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
 
 
  //  Software Guide : BeginLatex
  //
  //  Finally we trigger the execution of the registration method by calling
  //  the \code{Update()} method. The call is placed in a \code{try/catch}
  //  block in the case any exceptions are thrown.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  try
  {
    registration->Update();
    std::cout << "Optimizer stop condition: "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Once the optimization converges, we recover the parameters from the
  //  registration method. We can also recover the
  //  final value of the metric with the \code{GetValue()} method and the
  //  final number of iterations with the \code{GetCurrentIteration()}
  //  method.
  //
  //  \index{itk::RegistrationMethodv4!GetValue()}
  //  \index{itk::RegistrationMethodv4!GetCurrentIteration()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const TransformType::ParametersType finalParameters =
    registration->GetOutput()->Get()->GetParameters();
 
  const double finalRotationCenterX = transform->GetCenter()[0];
  const double finalRotationCenterY = transform->GetCenter()[1];
  const double finalTranslationX = finalParameters[4];
  const double finalTranslationY = finalParameters[5];
 
  const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
  const double       bestValue = optimizer->GetValue();
  // Software Guide : EndCodeSnippet
 
 
  // Print out results
  //
  std::cout << "Result = " << std::endl;
  std::cout << " Center X      = " << finalRotationCenterX << std::endl;
  std::cout << " Center Y      = " << finalRotationCenterY << std::endl;
  std::cout << " Translation X = " << finalTranslationX << std::endl;
  std::cout << " Translation Y = " << finalTranslationY << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue << std::endl;
 
  // Compute the rotation angle and scaling from SVD of the matrix
  // \todo Find a way to figure out if the scales are along X or along Y.
  // VNL returns the eigenvalues ordered from largest to smallest.
 
  vnl_matrix<double> p(2, 2);
  p[0][0] = static_cast<double>(finalParameters[0]);
  p[0][1] = static_cast<double>(finalParameters[1]);
  p[1][0] = static_cast<double>(finalParameters[2]);
  p[1][1] = static_cast<double>(finalParameters[3]);
  vnl_svd<double>    svd(p);
  vnl_matrix<double> r(2, 2);
  r = svd.U() * vnl_transpose(svd.V());
  const double angle = std::asin(r[1][0]);
 
  const double angleInDegrees = angle * 180.0 / itk::Math::pi;
 
  std::cout << " Scale 1         = " << svd.W(0) << std::endl;
  std::cout << " Scale 2         = " << svd.W(1) << std::endl;
  std::cout << " Angle (degrees) = " << angleInDegrees << std::endl;
 
 
  //  Software Guide : BeginLatex
  //
  //  Let's execute this example over two of the images provided in
  //  \code{Examples/Data}:
  //
  //  \begin{itemize}
  //  \item \code{BrainProtonDensitySliceBorder20.png}
  //  \item \code{BrainProtonDensitySliceR10X13Y17.png}
  //  \end{itemize}
  //
  //  The second image is the result of intentionally rotating the first
  //  image by $10$ degrees and then translating by $(-13,-17)$.  Both images
  //  have unit-spacing and are shown in Figure
  //  \ref{fig:FixedMovingImageRegistration9}. We execute the code using the
  //  following parameters: step length=1.0, translation scale= 0.0001 and
  //  maximum number of iterations = 300. With these images and parameters
  //  the registration takes $92$ iterations and produces
  //
  //  \begin{center}
  //  \begin{verbatim}
  //   90  44.0851 [0.9849, -0.1729, 0.1725, 0.9848, 12.4541, 16.0759]
  //   AffineAngle: 9.9494
  //  \end{verbatim}
  //  \end{center}
  //
  //  These results are interpreted as
  //
  //  \begin{itemize}
  //  \item Iterations   = 92
  //  \item Final Metric = 44.0386
  //  \item Center       = $( 111.204,   131.591   )$ millimeters
  //  \item Translation  = $(   12.4542,  16.076 )$ millimeters
  //  \item Affine scales = $(1.00014, .999732)$
  //  \end{itemize}
  //
  //  The second component of the matrix values is usually associated with
  //  $\sin{\theta}$. We obtain the rotation through SVD of the affine
  //  matrix. The value is $9.9494$ degrees, which is approximately the
  //  intentional misalignment of $10.0$ degrees.
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
  // \itkcaption[AffineTransform registration]{Fixed and moving images
  // provided as input to the registration method using the AffineTransform.}
  // \label{fig:FixedMovingImageRegistration9}
  // \end{figure}
  //
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{ImageRegistration9Output}
  // \includegraphics[width=0.32\textwidth]{ImageRegistration9DifferenceBefore}
  // \includegraphics[width=0.32\textwidth]{ImageRegistration9DifferenceAfter}
  // \itkcaption[AffineTransform output images]{The resampled moving image
  // (left), and the difference between the fixed and moving images before
  // (center) and after (right) registration with the AffineTransform
  // transform.} \label{fig:ImageRegistration9Outputs} \end{figure}
  //
  // Figure \ref{fig:ImageRegistration9Outputs} shows the output of the
  // registration. The right most image of this figure shows the squared
  // magnitude difference between the fixed image and the resampled
  // moving image.
  //
  // \begin{figure}
  // \center
  // \includegraphics[height=0.32\textwidth]{ImageRegistration9TraceMetric}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration9TraceAngle}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration9TraceTranslations}
  // \itkcaption[AffineTransform output plots]{Metric values,
  // rotation angle and translations during the registration using the
  // AffineTransform transform.}
  // \label{fig:ImageRegistration9Plots}
  // \end{figure}
  //
  //  Figure \ref{fig:ImageRegistration9Plots} shows the plots of the main
  //  output parameters of the registration process. The metric values at
  //  every iteration are shown on the left plot. The angle values are shown
  //  on the middle plot, while the translation components of the registration
  //  are presented on the right plot. Note that the final total offset of the
  //  transform is to be computed as a combination of the shift due to
  //  rotation plus the explicit translation set on the transform.
  //
  //  Software Guide : EndLatex
 
 
  //  The following code is used to dump output images to files.
  //  They illustrate the final results of the registration.
  //  We will resample the moving image and write out the difference image
  //  before and after registration. We will also rescale the intensities of
  //  the difference images, so that they look better!
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  auto resampler = ResampleFilterType::New();
 
  resampler->SetTransform(transform);
  resampler->SetInput(movingImageReader->GetOutput());
 
  const FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
 
  resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resampler->SetOutputOrigin(fixedImage->GetOrigin());
  resampler->SetOutputSpacing(fixedImage->GetSpacing());
  resampler->SetOutputDirection(fixedImage->GetDirection());
  resampler->SetDefaultPixelValue(100);
 
  using OutputPixelType = unsigned char;
 
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
 
  using CastFilterType =
    itk::CastImageFilter<FixedImageType, OutputImageType>;
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
 
  auto writer = WriterType::New();
  auto caster = CastFilterType::New();
 
 
  writer->SetFileName(argv[3]);
 
 
  caster->SetInput(resampler->GetOutput());
  writer->SetInput(caster->GetOutput());
  writer->Update();
 
 
  using DifferenceFilterType =
    itk::SubtractImageFilter<FixedImageType, FixedImageType, FixedImageType>;
 
  auto difference = DifferenceFilterType::New();
 
  difference->SetInput1(fixedImageReader->GetOutput());
  difference->SetInput2(resampler->GetOutput());
 
  auto writer2 = WriterType::New();
 
  using RescalerType =
    itk::RescaleIntensityImageFilter<FixedImageType, OutputImageType>;
 
  auto intensityRescaler = RescalerType::New();
 
  intensityRescaler->SetInput(difference->GetOutput());
  intensityRescaler->SetOutputMinimum(0);
  intensityRescaler->SetOutputMaximum(255);
 
  writer2->SetInput(intensityRescaler->GetOutput());
  resampler->SetDefaultPixelValue(1);
 
  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc > 5)
  {
    writer2->SetFileName(argv[5]);
    writer2->Update();
  }
 
 
  using IdentityTransformType = itk::IdentityTransform<double, Dimension>;
  auto identity = IdentityTransformType::New();
 
  // Compute the difference image between the
  // fixed and moving image before registration.
  if (argc > 4)
  {
    resampler->SetTransform(identity);
    writer2->SetFileName(argv[4]);
    writer2->Update();
  }
 
  return EXIT_SUCCESS;
}